Fast and easy visualization of blood flow patterns in 4D Qflow MRI

To enable efficient fast and easy visualization of blood flow patterns in 4D Qflow MRI we have automated vessel segmentation and flow pattern visualization. The new methods enable flow pattern visualization within 10 seconds. As such, our method allows for routine clinical use for flow pattern visualization

Estimating central blood pressure from aortic flow: development and assessment of algorithms

Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data. NEW & NOTEWORTHY First, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available.This work was supported by a PhD Fellowship awarded by the King’s College London and Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging [EP/L015226/1], the British Heart Foundation (BHF) [PG/15/104/31913], and the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]. The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative at Guy’s and St Thomas’ NHS Foundation Trust (GSTT)

User interface for the processing and presentation of image data

\u3cp\u3eThe invention relates to a workstation provided with an interface for the processing of sets of diagnostic images. The diagnostic images may have been generated by various diagnostic imaging modalities such as X-ray, CT, MRI, ultrasound etc. Various image processing applications can be applied to each set of diagnostic images. For each set of diagnostic images there is formed a representative pictorial (small image showing coarse details only) is formed and displayed. Furthermore, a set of attributes is assigned to the respective sets of images. Such attributes determine the image processing applications that are feasible for the set of diagnostic images at issue. The applicable image processing applications are displayed together with each pictorial. Preferably, the combinations of applicable image processing applications and the pictorial are displayed in a tab-page structure which corresponds to the workflow in the relevant hospital department.\u3c/p\u3

An Integrated Software Application for Non-invasive Assessment of Local Aortic Haemodynamic Parameters:20th Conference on Medical Image Understanding and Analysis (MIUA 2016)

AbstractNon-invasive assessment of haemodynamic data, such as pressure and flow profiles, is helpful in detecting cardiac disease at an early stage. However, current methods lack spatial accuracy and do not take local variations into account. This paper presents a software tool that extracts the arterial geometry and blood inflow profiles from MR images, which are subsequently used to run a 1D haemodynamic simulation model, and displays its output. The workflow is highly automated but allows user-interaction to correct inaccuracies. The tool was evaluated for inter-observer agreement on one healthy volunteer, and results are shown for one patient with an aortic coarctation. The resulting haemodynamic parameters show high agreement between different users and reveal local changes within a coarctation patient

Aortic length measurements for pulse wave velocity calculation:manual 2D vs automated 3D centreline extraction

BACKGROUND: Pulse wave velocity (PWV) is a biomarker for the intrinsic stiffness of the aortic wall, and has been shown to be predictive for cardiovascular events. It can be assessed using cardiovascular magnetic resonance (CMR) from the delay between phase-contrast flow waveforms at two or more locations in the aorta, and the distance on CMR images between those locations. This study aimed to investigate the impact of different distance measurement methods on PWV. We present and evaluate an algorithm for automated centreline tracking in 3D images, and compare PWV calculations using distances derived from 3D images to those obtained from a conventional 2D oblique-sagittal image of the aorta. METHODS: We included 35 patients from a twin cohort, and 20 post-coarctation repair patients. Phase-contrast flow was acquired in the ascending, descending and diaphragmatic aorta. A 3D centreline tracking algorithm is presented and evaluated on a subset of 30 subjects, on three CMR sequences: balanced steady-state free precession (SSFP), black-blood double inversion recovery turbo spin echo, and contrast-enhanced CMR angiography. Aortic lengths are subsequently compared between measurements from a 2D oblique-sagittal plane, and a 3D geometry. RESULTS: The error in length of automated 3D centreline tracking compared with manual annotations ranged from 2.4 [1.8-4.3] mm (mean [IQR], black-blood) to 6.4 [4.7-8.9] mm (SSFP). The impact on PWV was below 0.5m/s (<5%). Differences between 2D and 3D centreline length were significant for the majority of our experiments (p < 0.05). Individual differences in PWV were larger than 0.5m/s in 15% of all cases (thoracic aorta) and 37% when studying the aortic arch only. Finally, the difference between end-diastolic and end-systolic 2D centreline lengths was statistically significant (p < 0.01), but resulted in small differences in PWV (0.08 [0.04 - 0.10]m/s). CONCLUSIONS: Automatic aortic centreline tracking in three commonly used CMR sequences is possible with good accuracy. The 3D length obtained from such sequences can differ considerably from lengths obtained from a 2D oblique-sagittal plane, depending on aortic curvature, adequate planning of the oblique-sagittal plane, and patient motion between acquisitions. For accurate PWV measurements we recommend using 3D centrelines

Magnetic resonance imaging planning in children with complex congenital heart disease:a new approach

\u3cp\u3eOBJECTIVES: To compare a standard sequential 2D Planning Method (2D-PM) with a 3D offline Planning Method (3D-PM) based on 3D contrast-enhanced magnetic resonance angiography (CE-MRA) in children with congenital heart disease (CHD).\u3c/p\u3e\u3cp\u3eDESIGN: In 14 children with complex CHD (mean: 2.6 years, range: 3 months to 7.6 years), axial and coronal cuts were obtained with single slice spin echo sequences to get the final double oblique longitudinal cut of the targeted anatomical structure (2D-PM, n = 31). On a separate workstation, similar maximal intensity projection (MIP) images were generated offline from a 3D CE-MRA. MIP images were localizers for repeated targeted imaging using the previous spin echo sequence (3D-PM). Finally, image coverage, spatial orientation and acquisition time were compared for 2D-PM and 3D-PM.\u3c/p\u3e\u3cp\u3eMAIN OUTCOME MEASURES: 2D-PM and 3D-PM images were similar: both perfectly covered the selected anatomic regions and no spatial differences were found (p&gt;0.05). The mean time for creation of the final imaging plane was 241 ± 31 s (2D-PM) compared to 71 ± 18 s (3D-PM) (p&lt;0.05).\u3c/p\u3e\u3cp\u3eCONCLUSIONS: 3D-PM shows similar results compared to 2D-PM, but allows faster and offline planning thereby reducing the scan time significantly. As newly developed high-resolution 3D datasets can also be used further improvement of this technology is expected.\u3c/p\u3

Remote Collaboration, Decision Support, and On-Demand Medical Image Analysis for Acute Stroke Care

Part 2: Industry TrackInternational audienceAcute stroke is the leading cause of disabilities and the fourth cause of death worldwide. The treatment of stroke patients often requires fast collaboration between medical experts and fast analysis and sharing of large amounts of medical data, especially image data. In this situation, cloud technologies provide a potentially cost-effective way to optimize management of stroke patients and, consequently, improve patient outcome. This paper presents a cloud-based platform for Medical Distributed Utilization of Services & Applications (MEDUSA). This platform aims at improving current acute care settings by allowing fast medical data exchange, advanced processing of medical image data, automated decision support, and remote collaboration between physicians in a secure and responsive virtual space. We describe a prototype implemented in the MEDUSA platform for supporting the treatment of acute stroke patients. As the initial evaluation illustrates, this prototype improves several aspects of current stroke care and has the potential to play an important role in the care management of acute stroke patients

Remote Collaboration, Decision Support, and On-Demand Medical Image Analysis for Acute Stroke Care

Acute stroke is the leading cause of disabilities and the fourth cause of death worldwide. The treatment of stroke patients often requires fast collaboration between medical experts and fast analysis and sharing of large amounts of medical data, especially image data. In this situation, cloud technologies provide a potentially cost-effective way to optimize management of stroke patients and, consequently, improve patient outcome. This paper presents a cloud-based platform for Medical Distributed Utilization of Services & Applications (MEDUSA). This platform aims at improving current acute care settings by allowing fast medical data exchange, advanced processing of medical image data, automated decision support, and remote collaboration between physicians in a secure and responsive virtual space. We describe a prototype implemented in the MEDUSA platform for supporting the treatment of acute stroke patients. As the initial evaluation illustrates, this prototype improves several aspects of current stroke care and has the potential to play an important role in the care management of acute stroke patient